Abstract
Clustering is a fundamental unsupervised representation learning task with wide application in computer vision and pattern recognition. Deep clustering utilizes deep neural networks to learn latent representation, which is suitable for clustering. However, previous deep clustering methods, especially image clustering, focus on the features of the data itself and ignore the relationship between the data, which is crucial for clustering. In this paper, we propose a novel Deep Structure and Attention aware Subspace Clustering (DSASC), which simultaneously considers data content and structure information. We use a vision transformer to extract features, and the extracted features are divided into two parts, structure features, and content features. The two features are used to learn a more efficient subspace structure for spectral clustering. Extensive experimental results demonstrate that our method significantly outperforms state-of-the-art methods. Our code will be available at https://github.com/cs-whh/DSASC.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bo, D., Wang, X., Shi, C., Zhu, M., Lu, E., Cui, P.: Structural deep clustering network. In: Proceedings of the Web Conference 2020, pp. 1400–1410 (2020)
Cai, D., He, X., Wang, X., Bao, H., Han, J.: Locality preserving nonnegative matrix factorization. In: Twenty-first International Joint Conference on Artificial Intelligence, pp. 1010–1015 (2009)
Cai, J., Fan, J., Guo, W., Wang, S., Zhang, Y., Zhang, Z.: Efficient deep embedded subspace clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1–10 (2022)
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I, pp. 213–229. Springer International Publishing, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_13
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Adv. Neural. Inf. Process. Syst. 33, 9912–9924 (2020)
Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9650–9660 (2021)
Chen, X., Cai, D.: Large scale spectral clustering with landmark-based representation. In: Twenty-fifth AAAI Conference on Artificial Intelligence (2011)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2020)
Elhamifar, E., Vidal, R.: Sparse subspace clustering: Algorithm, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2765–2781 (2013)
Fard, M.M., Thonet, T., Gaussier, E.: Deep k-means: jointly clustering with k-means and learning representations. Pattern Recogn. Lett. 138, 185–192 (2020)
Guo, X., Gao, L., Liu, X., Yin, J.: Improved deep embedded clustering with local structure preservation. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp. 1753–1759 (2017)
Ji, P., Zhang, T., Li, H., Salzmann, M., Reid, I.: Deep subspace clustering networks. In:Advances in Neural Information Processing Systems 30 (2017)
Jiang, Z., Zheng, Y., Tan, H., Tang, B., Zhou, H.: Variational deep embedding: an unsupervised and generative approach to clustering. arXiv preprint arXiv:1611.05148 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Liu, G., Lin, Z., Yan, S., Sun, J., Yu, Y., Ma, Y.: Robust recovery of subspace structures by low-rank representation. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 171–184 (2012)
Lloyd, S.: Least squares quantization in pcm. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)
Peng, Z., Liu, H., Jia, Y., Hou, J.: Adaptive attribute and structure subspace clustering network. IEEE Trans. Image Process. 31, 3430–3439 (2022)
Sadeghi, M., Armanfard, N.: Deep successive subspace learning for data clustering. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2021)
Sadeghi, M., Armanfard, N.: Deep multirepresentation learning for data clustering. IEEE Transactions on Neural Networks and Learning Systems (2023)
Sheng, G., Wang, Q., Pei, C., Gao, Q.: Contrastive deep embedded clustering. Neurocomputing 514, 13–20 (2022)
Strudel, R., Garcia, R., Laptev, I., Schmid, C.: Segmenter: transformer for semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7262–7272 (2021)
Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, pp. 478–487. PMLR (2016)
Yang, B., Fu, X., Sidiropoulos, N.D., Hong, M.: Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In: International Conference on Machine Learning, pp. 3861–3870. PMLR (2017)
You, C., Li, C.G., Robinson, D.P., Vidal, R.: Oracle based active set algorithm for scalable elastic net subspace clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3928–3937 (2016)
Zhang, S., You, C., Vidal, R., Li, C.G.: Learning a self-expressive network for subspace clustering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12393–12403 (2021)
Zhang, X., Liu, H., Li, Q., Wu, X.M.: Attributed graph clustering via adaptive graph convolution. arXiv preprint arXiv:1906.01210 (2019)
Acknowledgements
The authors would like to thank the editors and the anonymous reviewers for their constructive comments and suggestions. This paper is supported by the National Natural Science Foundation of China (Grant Nos. 61972264, 62072312), Natural Science Foundation of Guangdong Province (Grant No. 2019A1515010894) and Natural Science Foundation of Shenzhen (Grant No. 20200807165235002).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wu, W., Wang, W., Kong, S. (2024). Deep Structure and Attention Aware Subspace Clustering. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14428. Springer, Singapore. https://doi.org/10.1007/978-981-99-8462-6_12
Download citation
DOI: https://doi.org/10.1007/978-981-99-8462-6_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-8461-9
Online ISBN: 978-981-99-8462-6
eBook Packages: Computer ScienceComputer Science (R0)